An Ultra-High Field Magnetic Resonance Spectroscopy Study of Post Exercise Lactate, Glutamate and Glutamine Change in the Human Brain
Bibliographic record
Abstract
During strenuous exercise there is a progressive increase in lactate uptake and metabolism into the brain as workload and plasma lactate levels increase. Although it is now widely accepted that the brain can metabolize lactate, few studies have directly measured brain lactate following vigorous exercise. Here, we used ultra-high field magnetic resonance spectroscopy of the brain to obtain static measures of brain lactate, as well as brain glutamate and glutamine after vigorous exercise. The aims of our experiment were to (a) track the changes in brain lactate following recovery from exercise, and (b) to simultaneously measure the signals from brain glutamate and glutamine. The results of our experiment showed that vigorous exercise resulted in a significant increase in brain lactate. Furthermore, both glutamate and glutamine were successfully resolved, and as expected, although contrary to some previous reports, we did not observe any significant change in either amino acid after exercise. We did however observe a negative correlation between glutamate and a measure of fitness. These results support the hypothesis that peripherally derived lactate is taken up by the brain when available. Our data additionally highlight the potential of ultra-high field MRS as a non-invasive way of measuring multiple brain metabolite changes with exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".